Patients with Parkinson's disease and a history of falls have decreased cerebellar grey matter volumes in the cognitive cerebellum

Investigation of total cerebellar and flocculonodular lobe volume in Parkinson's disease and healthy individuals: a brain segmentation study

BACKGROUND

Parkinson's disease (PD) is a neurodegenerative disorder with an unexplored link to the cerebellum. In the pathophysiology of balance disorders in PD, the role of the flocculonodular lobe (FL) is linked to the impairment of the dopaminergic system. Dopamine deficiency can also lead to changes in cerebellum functions, disrupting balance control. This study compares cerebellar and FL volumes between healthy controls (HC) and PD patients, analyzing their correlation with clinical outcomes.

METHODS

We used magnetic resonance images of 23 PD patients (14 male, 9 female) and 24 HC (9 male, 15 female). Intracranial (ICV), total cerebellar, FL, and cerebellar gray matter volumes were measured using VolBrain. Clinical outcomes in PD patients were assessed using the Unified Parkinson's Disease Rating Scale (UPDRS-III) to evaluate motor function, with scores correlated to volumetric data.

RESULTS

The cerebellar and gray matter volumes in HC were 115.53 ± 10.44 cm3 and 84.83 ± 7.76 cm3, respectively, compared to 126.83 ± 13.47 cm3 and 92.37 ± 9.45 cm3 in PD patients, indicating significantly larger volumes in PD patients (p < 0.05). The flocculonodular lobe gray matter volume was 1.14 ± 0.19 cm3 in PD patients and 1.02 ± 0.13 cm3 in HC, but there was a significant increase in gray matter volume in PD patients between the groups (p < 0.05). In PD patients, significant negative correlations were observed between FL volume and the UPDRS-III scores (r =  - 0.467, p = 0.033) and between UPDRS-III scores and both total (r =  - 0.453, p = 0.039) and normalized (r =  - 0.468, p = 0.032) gray matter volumes of the FL.

CONCLUSION

Although total gray matter volumes were larger in PD patients, the volumes of FL did not differ between groups. In Parkinson's disease, increased cerebellar volume may regulate fine motor movements rather than balance.

Cognitive impairment associated with cerebellar volume loss in spinocerebellar ataxia type 3

BACKGROUND

Many neuroscience and neurology studies have forced a reconsideration of the traditional motor-related scope of cerebellar function, which has now expanded to include various cognitive functions. Spinocerebellar ataxia type 3 (SCA3; the most common hereditary ataxia) is neuropathologically characterized by cerebellar atrophy and frequently presents with cognitive impairment.

OBJECTIVE

To characterize cognitive impairment in SCA3 and investigate the cerebellum-cognition associations.

METHODS

This prospective, cross-sectional cohort study recruited 126 SCA3 patients and 41 healthy control individuals (HCs). Participants underwent a brain 3D T1-weighted images as well as neuropsychological tests. Voxel-based morphometry (VBM) and region of interest (ROI) approaches were performed on the 3D T1-weighted images. CERES was used to automatically segment cerebellums. Patients were grouped into cognitively impaired (CI) and cognitively preserved (CP), and clinical and MRI parameters were compared. Multivariable regression models were fitted to examine associations between cerebellar microstructural alterations and cognitive domain impairments.

RESULTS

Compared to HCs, SCA3 patients showed cognitive domain impairments in information processing speed, verbal memory, executive function, and visuospatial perception. Between CI and CP subgroups, the CI subgroup was older and had lower education, as well as higher severity scores. VBM and ROI analyses revealed volume loss in cerebellar bilateral lobule VI, right lobule Crus I, and right lobule IV of the CI subgroup, and all these cerebellar lobules were associated with the above cognitive domain impairments.

CONCLUSIONS

Our findings demonstrate the multiple cognitive domain impairments in SCA3 patients and indicate the responsible cerebellar lobules for the impaired cognitive domain(s).

A review of emergent intelligent systems for the detection of Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder affecting people worldwide. The PD symptoms are divided into motor and non-motor symptoms. Detection of PD is very crucial and essential. Such challenges can be overcome by applying artificial intelligence to diagnose PD. Many studies have also proposed the implementation of computer-aided diagnosis for the detection of PD. This systematic review comprehensively analyzed all appropriate algorithms for detecting and assessing PD based on the literature from 2012 to 2023 which are conducted as per PRISMA model. This review focused on motor symptoms, namely handwriting dynamics, voice impairments and gait, multimodal features, and brain observation using single photon emission computed tomography, magnetic resonance and electroencephalogram signals. The significant challenges are critically analyzed, and appropriate recommendations are provided. The critical discussion of this review article can be helpful in today's PD community in such a way that it allows clinicians to provide proper treatment and timely medication.

Differences in fall-related characteristics across cognitive disorders

Approximately 40-60% of falls in the elderly lead to injuries, resulting in disability and loss of independence. Despite the higher prevalence of falls and morbidity rates in cognitively impaired individuals, most fall risk assessments fail to account for mental status. In addition, successful fall prevention programmes in cognitively normal adults have generally failed in patients with cognitive impairment. Identifying the role of pathological aging on fall characteristics can improve the sensitivity and specificity of fall prevention approaches. This literature review provides a thorough investigation into fall prevalence and fall risk factors, the accuracy of fall risk assessments, and the efficacy of fall prevention strategies in individuals with diverse cognitive profiles. We show that fall-related characteristics differ between cognitive disorders and fall risk assessment tools as well as fall prevention strategies should critically consider each patient's cognitive status to facilitate the identification of fallers at an earlier stage and support clinical decision-making.